Nuclear resonance fluorescence (NRF) has been considered as a promising method for cargo inspection. Almost all isotopes existing in nature yield a unique NRF spectral signature. NRF signals obtained during cargo inspection are aggregates of various signatures from materials hidden inside. The challenge is to identify individual signatures embedded in this signature aggregation. Background noise and spectra overlap to further complicate the NRF signal analysis. This paper addresses these concerns through an intelligent methodology recognizing signature spectra and, subsequently, identifying cargo materials. The methodology relies on fuzzy logic for pattern identification and evaluation of the weighted options involved in decision making. The intelligent methodology is presented using different simulated NRF signal scenarios. The results obtained demonstrate that the algorithm is highly accurate in most spectra carrying a signal-to-noise ratio (SNR) >20 db. Misses and false alarms were observed for isotopes with only one NRF peak (lead) with SNR <35 db. Extensive parameter testing under different scenarios indicated the existence of parameter couples that maximize the accuracy even for SNR values <20 db. In all cases the algorithm execution time was <0.1 s and was significantly faster than that of the maximum likelihood algorithm.